• pandas 学习(2): pandas 数据结构之DataFrame


      DataFrame 类型类似于数据库表结构的数据结构,其含有行索引和列索引,可以将DataFrame 想成是由相同索引的Series组成的Dict类型。在其底层是通过二维以及一维的数据块实现。

    1.  DataFrame 对象的构建

      1.1 用包含等长的列表或者是NumPy数组的字典创建DataFrame对象

    In [68]: import pandas as pd
    
    In [69]: from pandas import Series,DataFrame
    
    # 建立包含等长列表的字典类型 In [
    70]: data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],'year': [2000, 2001, 20 ...: 02, 2001, 2002],'pop': [1.5, 1.7, 3.6, 2.4, 2.9]} In [71]: data Out[71]: {'pop': [1.5, 1.7, 3.6, 2.4, 2.9], 'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002]} # 建立DataFrame对象 In [72]: frame1 = DataFrame(data) # 红色部分为自动生成的索引 In [73]: frame1 Out[73]: pop state year 0 1.5 Ohio 2000 1 1.7 Ohio 2001 2 3.6 Ohio 2002 3 2.4 Nevada 2001 4 2.9 Nevada 2002

      在建立过程中可以指点列的顺序:

    In [74]: frame1 = DataFrame(data,columns=['year', 'state', 'pop'])
    
    In [75]: frame1
    Out[75]: 
       year   state  pop
    0  2000    Ohio  1.5
    1  2001    Ohio  1.7
    2  2002    Ohio  3.6
    3  2001  Nevada  2.4
    4  2002  Nevada  2.9

      和Series一样,DataFrame也是可以指定索引内容:

    In [76]: ind = ['one', 'two', 'three', 'four', 'five']
    In [77]: frame1 = DataFrame(data,index = ind)
    
    In [78]: frame1
    Out[78]: 
           pop   state  year
    one    1.5    Ohio  2000
    two    1.7    Ohio  2001
    three  3.6    Ohio  2002
    four   2.4  Nevada  2001
    five   2.9  Nevada  2002

      1.2.  用由字典类型组成的嵌套字典类型来生成DataFrame对象

      当由嵌套的字典类型生成DataFrame的时候,外部的字典索引会成为列名,内部的字典索引会成为行名。生成的DataFrame会根据行索引排序

    In [84]: pop = {'Nevada': {2001: 2.4, 2002: 2.9},'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
    
    In [85]: frame3 = DataFrame(pop)
    
    In [86]: frame3
    Out[86]: 
          Nevada  Ohio
    2000     NaN   1.5
    2001     2.4   1.7
    2002     2.9   3.6

      除了使用默认的按照行索引排序之外,还可以指定行序列:

    In [95]: frame3 = DataFrame(pop,[2002,2001,2000])
    
    In [96]: frame3
    Out[96]: 
          Nevada  Ohio
    2002     2.9   3.6
    2001     2.4   1.7
    2000     NaN   1.5

      1.3 其它构造方法:

      

    2.  DataFrame 内容访问

      从DataFrame中获取一列的结果为一个Series,可以通过以下两种方式获取:

    # 以字典索引方式获取
    In [100]: frame1["state"] Out[100]: one Ohio two Ohio three Ohio four Nevada five Nevada Name: state, dtype: object # 以属性方式获取 In [101]: frame1.state Out[101]: one Ohio two Ohio three Ohio four Nevada five Nevada Name: state, dtype: object

      也可以通过ix获取一行数据:

    In [109]: frame1.ix["one"] # 或者是 frame1.ix[0]
    Out[109]: 
    pop       1.5
    state    Ohio
    year     2000
    Name: one, dtype: object
    # 获取多行数据
    In [110]: frame1.ix[["tow","three","four"]]
    Out[110]:
           pop   state    year
    tow    NaN     NaN     NaN
    three  3.6    Ohio  2002.0
    four   2.4  Nevada  2001.0
    # 还可以通过默认数字行索引来获取数据
    In [111]: frame1.ix[range(3)]
    Out[111]:
           pop state  year
    one    1.5  Ohio  2000
    two    1.7  Ohio  2001
    three  3.6  Ohio  2002

      获取指定行,指定列的交汇值:

    In [119]: frame1["state"]
    Out[119]: 
    one        Ohio
    two        Ohio
    three      Ohio
    four     Nevada
    five     Nevada
    Name: state, dtype: object
    
    In [120]: frame1["state"][0]
    Out[120]: 'Ohio'
    
    In [121]: frame1["state"]["one"]
    Out[121]: 'Ohio'

      先指定列再指定行:

    In [125]: frame1.ix[0]
    Out[125]: 
    pop       1.5
    state    Ohio
    year     2000
    Name: one, dtype: object
    
    In [126]: frame1.ix[0]["state"]
    Out[126]: 'Ohio'
    
    In [127]: frame1.ix["one"]["state"]
    Out[127]: 'Ohio'
    
    In [128]: frame1.ix["one"][0]
    Out[128]: 1.5
    
    In [129]: frame1.ix[0][0]
    Out[129]: 1.5

    3. DataFrame 对象的修改

      增加一列,并所有赋值为同一个值:

    # 增加一列值
    In [131]: frame1["debt"] = 10 In [132]: frame1 Out[132]: pop state year debt one 1.5 Ohio 2000 10 two 1.7 Ohio 2001 10 three 3.6 Ohio 2002 10 four 2.4 Nevada 2001 10 five 2.9 Nevada 2002 10
    # 更改一列的值 In [
    133]: frame1["debt"] = np.arange(5) In [134]: frame1 Out[134]: pop state year debt one 1.5 Ohio 2000 0 two 1.7 Ohio 2001 1 three 3.6 Ohio 2002 2 four 2.4 Nevada 2001 3 five 2.9 Nevada 2002 4

      追加类型为Series的一列

    # 判断是否为东部区
    In [137]: east = (frame1.state == "Ohio") In [138]: east Out[138]: one True two True three True four False five False Name: state, dtype: bool # 赋Series值 In [139]: frame1["east"] = east In [140]: frame1 Out[140]: pop state year debt east one 1.5 Ohio 2000 0 True two 1.7 Ohio 2001 1 True three 3.6 Ohio 2002 2 True four 2.4 Nevada 2001 3 False five 2.9 Nevada 2002 4 False

      DataFrame 的行可以命名,同时多列也可以命名:

    In [145]: frame3.columns.name = "state"
    
    In [146]: frame3.index.name = "year"
    
    In [147]: frame3
    Out[147]: 
    state  Nevada  Ohio
    year               
    2002      2.9   3.6
    2001      2.4   1.7
    2000      NaN   1.5
  • 相关阅读:
    WSL 修改默认登录用户为root
    WSL ssh服务自启动
    odoo 获取model的所有字段
    C++类型父类与子类的转换--dynamic_cast(转)
    开源软件/镜像库
    C/C++编译器gcc的windows版本MinGW-w64安装教程(转)
    msys2 环境搭建
    Windows下利用Cygwin搭建C/C++开发环境GCC(转)
    模板类中子类访问父类中的成员需要通过this指针
    C++ 构造函数与this指针
  • 原文地址:https://www.cnblogs.com/linux-wangkun/p/5903945.html
Copyright © 2020-2023  润新知